Prediction of Protein-Protein Interaction By Metasample-Based Sparse Representation

الملخص EN

Protein-protein interactions (PPIs) play key roles in many cellular processes such as transcription regulation, cell metabolism, and endocrine function.

Understanding these interactions takes a great promotion to the pathogenesis and treatment of various diseases.

A large amount of data has been generated by experimental techniques; however, most of these data are usually incomplete or noisy, and the current biological experimental techniques are always very time-consuming and expensive.

In this paper, we proposed a novel method (metasample-based sparse representation classification, MSRC) for PPIs prediction.

A group of metasamples are extracted from the original training samples and then use the l1-regularized least square method to express a new testing sample as the linear combination of these metasamples.

PPIs prediction is achieved by using a discrimination function defined in the representation coefficients.

The MSRC is applied to PPIs dataset; it achieves 84.9% sensitivity, and 94.55% specificity, which is slightly lower than support vector machine (SVM) and much higher than naive Bayes (NB), neural networks (NN), and k-nearest neighbor (KNN).

The result shows that the MSRC is efficient for PPIs prediction.